Large Language Model Architect

Accenture; Ahmedabad District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Accenture is seeking a highly experienced Large Language Model Architect to lead end-to-end AI architecture for enterprise deployments. You will shape strategy for LLMs, RAG, and agentic systems, translating business goals into phased technical roadmaps and governance practices.

Deep expertise in Snowflake Cortex AI and related technologies is required, with focus on scalable deployment, RBAC, data governance, and secure, observable platforms across regulated industries.

Qualifications

• Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
• Minimum 12+ years of overall experience in software engineering, data engineering, AI/ML engineering, cloud architecture or enterprise technology architecture.
• Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.
• Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration and evaluation patterns.
• Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in production or enterprise-scale environments.
• Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.
• Demonstrated experience as a senior architect in industry domains such as banking, insurance, retail, healthcare, travel, logistics or telecom, with ability to align technology choices to business, risk and compliance expectations.

Responsibilities

• Lead / Senior Architect in AI LLM Technology Architecture, serving as the definitive technical authority for end-to-end AI architecture on Snowflake.
• Own the complete AI platform architecture spanning classical ML, generative AI, LLM applications, RAG, agentic systems, context engineering, model platforms, inference and enterprise AI integration.
• Operate at executive and senior stakeholder level, connecting business goals, industry priorities and transformation agendas to a coherent technical vision and sequenced AI implementation roadmap.
• Bring practical industry experience in banking, insurance, retail, healthcare, travel, logistics or telecom to ensure AI architectures address domain realities, regulatory expectations, security constraints, operational processes and measurable business outcomes.
• Lead and integrate work across multiple domain architects and subject matter specialists, resolving cross-domain design decisions and ensuring the full AI solution is cohesive, technically sound and enterprise-ready.
• Partner with CIOs, CTOs, business leaders and delivery stakeholders to shape enterprise AI strategy and convert business priorities into phased technical roadmaps.
• Lead enterprise AI architecture assessments, target-state definition, gap analysis, platform selection, modernization opportunities and implementation sequencing.
• For Snowflake, set the enterprise direction for Snowflake-native AI platforms define Cortex-based agent, RAG, document intelligence and analytics architectures within Snowflake governance perimeter establish Snowpark and Streamlit application reference patterns define RBAC, masking, lineage, monitoring, cost controls and data security standards for regulated AI workloads.
• Own the end-to-end technical solution for complex AI platforms, ensuring all domains are aligned to business objectives, enterprise standards and non-functional requirements.
• Define architectural direction for model- and tool-agnostic multi-agent systems including orchestration, memory, tool/skill use, agent registry, AI gateway/control-plane patterns and service abstraction.
• Establish the enterprise context layer architecture spanning knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly and conversation state management.
• Set security, governance, observability, performance, scalability and reliability standards across AI solution domains, including identity, authorization, PII protection, layered guardrails, auditability and evaluation gates.
• Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, latency, model quality, cost, safety, reliability and production support metrics.
• Drive architecture decisions for high-throughput, low-latency inference, model routing, model adaptation/fine-tuning, caching, cost controls and production-ready deployment platforms.
• Produce and own authoritative architecture artifacts including blueprints, ADRs, sequence diagrams, design specifications, integration patterns, reusable reference architectures and governance playbooks.

Skills

Generative AI

Snowflake Data Warehouse

Education

15 years full time education

Job description

Project Role : Large Language Model Architect

Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.

Must have skills : Generative AI

Good to have skills : Snowflake Data Warehouse

Minimum 18 year(s) of experience is required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

• Lead / Senior Architect in AI LLM Technology Architecture , serving as the definitive technical authority for end-to-end AI architecture on Snowflake.


• Own the complete AI platform architecture spanning classical machine learning, generative AI, LLM applications, RAG, agentic systems, context engineering, model platforms, inference and enterprise AI integration.


• Operate at executive and senior stakeholder level, connecting business goals, industry priorities and transformation agendas to a coherent technical vision and sequenced AI implementation roadmap.


• Bring practical industry experience in banking, insurance, retail, healthcare, travel, logistics or telecom to ensure AI architectures address domain realities, regulatory expectations, security constraints, operational processes and measurable business outcomes.


• Lead and integrate work across multiple domain architects and subject matter specialists, resolving cross-domain design decisions and ensuring the full AI solution is cohesive, technically sound and enterprise-ready.

Key Responsibilities

• Partner with CIOs, CTOs, business leaders and delivery stakeholders to shape enterprise AI strategy and convert business priorities into phased technical roadmaps.


• Lead enterprise AI architecture assessments, target-state definition, gap analysis, platform selection, modernization opportunities and implementation sequencing.


• For Snowflake, set the enterprise direction for Snowflake-native AI platforms define Cortex-based agent, RAG, document intelligence and analytics architectures within Snowflake governance perimeter establish Snowpark and Streamlit application reference patterns define RBAC, masking, lineage, monitoring, cost controls and data security standards for regulated AI workloads.


• Own the end-to-end technical solution for complex AI platforms, ensuring all domains are aligned to business objectives, enterprise standards and non-functional requirements.


• Define architectural direction for model- and tool-agnostic multi-agent systems including orchestration, memory, tool/skill use, agent registry, AI gateway/control-plane patterns and service abstraction.


• Establish the enterprise context layer architecture spanning knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly and conversation state management.


• Set security, governance, observability, performance, scalability and reliability standards across AI solution domains, including identity, authorization, PII protection, layered guardrails, auditability and evaluation gates.


• Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, latency, model quality, cost, safety, reliability and production support metrics.


• Drive architecture decisions for high-throughput, low-latency inference, model routing, model adaptation/fine-tuning, caching, cost controls and production-ready deployment platforms.


• Produce and own authoritative architecture artifacts including blueprints, ADRs, sequence diagrams, design specifications, integration patterns, reusable reference architectures and governance playbooks.

Required Qualifications

• Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.


• Minimum 12+ years of overall experience in software engineering, data engineering, AI/ML engineering, cloud architecture or enterprise technology architecture.


• Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.


• Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration and evaluation patterns.


• Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in production or enterprise-scale environments.


• Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.


• Demonstrated experience as a senior architect in industry domains such as banking, insurance, retail, healthcare, travel, logistics or telecom, with ability to align technology choices to business, risk and compliance expectations.

Required Skills/ Experience

• Deep architecture and hands-on engineering experience with Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions/LLM Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies, access history, lineage and observability capabilities.


• Strong ability to shape enterprise-grade AI architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing and GenAI evaluation.


• Experience defining NFRs and architecture controls for performance, scalability, security, privacy, governance, observability, resiliency, cost optimization and operational readiness.


• Ability to compare platforms, frameworks, foundation models and deployment patterns, making evidence-based technology recommendations and defensible architecture trade-offs.


• Experience establishing AI gateway/control-plane patterns, agent registry and certification gates, authorization models, layered guardrails, model risk controls and production governance.


• Strong stakeholder management and thought leadership skills with ability to communicate architecture decisions to executives, product leaders, security teams and engineering delivery teams.

Good to Have Skills

• SnowPro Advanced Architect, SnowPro Advanced Data Engineer or Snowflake ML exposure exposure to Snowpark Python, Streamlit, semantic models, dbt, Native Apps, data sharing, Cortex Guardrails and Snowflake cost/performance tuning.


• Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.


• Experience with responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing and GenAI FinOps.


• Recognized thought leadership through reusable architecture assets, platform accelerators, whitepapers, client advisory, internal capability building or conference/community participation.

15 years full time education

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